To resolve many business-related questions, a tool referred to as multidimensional analysis is used, which in SQL terms is a ‘group by’ operation. Generally for one query, a large amount of data is involved, whereby computing performance is critical to obtain the results, e.g., users cannot wait several hours to get analysis results.
Current OLAP (Online Analytical Processing) systems enhance the performance by pre-computing data cubes that correspond to the multidimensional arrangement of the data to be analyzed. More particularly, in OLAP, a dimension is a category of data represented in one column of a table, and a measure represents data in the table that can be accessed by specifying values for its dimensions. A set of measures having the same dimensions may be represented as an OLAP cube.
However, as the number of dimensions increases, the storage required for data cubes grows exponentially. As a result of this limitation, one cube can only support tens of dimensions. There was heretofore no known effective tool that is able to support an analysis of high-dimensional data, such as data having thousands of dimensions, yet such data exists in a number of situations for which data analysis is desired.